Founders’ Formula: Scientists and Venture Financing Dynamics
Bibliographic record
Abstract
New science- and technology-based ventures are heavily dependent on equity financing for their development, growth, and ultimate success. We explore how the proportion of scientists (PhD holders) on founding teams influences the likelihood of achieving exceptionally high equity financing performance. Using signaling and imprinting theories for explaining the mean and variability effects of equity funding, we theoretically demonstrate that positive outliers in equity financing are likely to be found among the science-based ventures with a medium level of scientists ratio (SR) in founding teams, with the outlier likelihood substantially decreasing in the low and high regions. To test the theoretical predictions, we use a unique dataset from a prominent accelerator of science-based new ventures, which incorporates multiple streams and multiple sites globally. Our findings reveal that the scientists’ ratio (SR) exhibits an inverse U-shaped relationship with both the mean level of equity funding (maximum reached at SR = 0.50) and the variance of equity funding (maximum reached at SR = 0.66), resulting in the highest probability of securing outlier equity funding (top-1%) at an SR of 0.56. In additional analyses, we also demonstrate that an alternative financing mechanism of grant funding demonstrates a different pattern: Although SR also follows an inverse U-shaped relationship with respect to the mean and variance of grant funding, the maximum impacts occur at extremely high SR levels. This suggests that as the SR on founding teams increases, the likelihood of securing exceptionally high grant funding continues to rise without the diminishing returns observed in equity financing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".